



'''simple docstring'''


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class   __UpperCamelCase							(SCREAMING_SNAKE_CASE_      ):
	__A      	=       (DDPMScheduler,)







	def 	_a (     self			,					**_lowerCAmelCase    )     ->     Any:
							'''simple docstring'''






							lowercase   =				{
							    """num_train_timesteps""": 1000,
							    """beta_start""": 0.0001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							    """variance_type""": """fixed_small""",
							    """clip_sample""": True,
							}

							config.update(**snake_case__    )
							return config







	def 	_a (     self    )     ->     Tuple:
							'''simple docstring'''






							for timesteps in [1, 5, 100, 1000]:
													self.check_over_configs(num_train_timesteps=snake_case__    )







	def 	_a (     self    )     ->     int:
							'''simple docstring'''






							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]			,					[0.002, 0.02, 0.2, 2]    ):
													self.check_over_configs(beta_start=snake_case__			,					beta_end=snake_case__    )







	def 	_a (     self    )     ->     Union[str, Any]:
							'''simple docstring'''






							for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=snake_case__    )







	def 	_a (     self    )     ->     Optional[int]:
							'''simple docstring'''






							for variance in ["fixed_small", "fixed_large", "other"]:
													self.check_over_configs(variance_type=snake_case__    )







	def 	_a (     self    )     ->     int:
							'''simple docstring'''






							for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=snake_case__    )







	def 	_a (     self    )     ->     List[Any]:
							'''simple docstring'''






							self.check_over_configs(thresholding=snake_case__    )
							for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=snake_case__			,					prediction_type=snake_case__			,					sample_max_value=snake_case__			,					)







	def 	_a (     self    )     ->     Any:
							'''simple docstring'''






							for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(prediction_type=snake_case__    )







	def 	_a (     self    )     ->     List[str]:
							'''simple docstring'''






							for t in [0, 500, 999]:
													self.check_over_forward(time_step=snake_case__    )







	def 	_a (     self    )     ->     Dict:
							'''simple docstring'''






							lowercase   =				self.scheduler_classes[0]
							lowercase   =				self.get_scheduler_config()
							lowercase   =				scheduler_class(**snake_case__    )

							assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.0_0979    )    ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.02    )    ) < 1E-5







	def 	_a (     self    )     ->     Dict:
							'''simple docstring'''






							lowercase   =				self.scheduler_classes[0]
							lowercase   =				self.get_scheduler_config()
							lowercase   =				scheduler_class(**snake_case__    )

							lowercase   =				len(snake_case__    )

							lowercase   =				self.dummy_model()
							lowercase   =				self.dummy_sample_deter
							lowercase   =				torch.manual_seed(0    )

							for t in reversed(range(snake_case__    )    ):
													# 1. predict noise residual
													lowercase   =				model(snake_case__			,					snake_case__    )

													# 2. predict previous mean of sample x_t-1
													lowercase   =				scheduler.step(snake_case__			,					snake_case__			,					snake_case__			,					generator=snake_case__    ).prev_sample

													# if t > 0:
													#     noise = self.dummy_sample_deter
													#     variance = scheduler.get_variance(t) ** (0.5) * noise
													#
													# sample = pred_prev_sample + variance
													lowercase   =				pred_prev_sample

							lowercase   =				torch.sum(torch.abs(snake_case__    )    )
							lowercase   =				torch.mean(torch.abs(snake_case__    )    )

							assert abs(result_sum.item() - 258.9606    ) < 1E-2
							assert abs(result_mean.item() - 0.3372    ) < 1E-3







	def 	_a (     self    )     ->     List[str]:
							'''simple docstring'''






							lowercase   =				self.scheduler_classes[0]
							lowercase   =				self.get_scheduler_config(prediction_type="""v_prediction"""    )
							lowercase   =				scheduler_class(**snake_case__    )

							lowercase   =				len(snake_case__    )

							lowercase   =				self.dummy_model()
							lowercase   =				self.dummy_sample_deter
							lowercase   =				torch.manual_seed(0    )

							for t in reversed(range(snake_case__    )    ):
													# 1. predict noise residual
													lowercase   =				model(snake_case__			,					snake_case__    )

													# 2. predict previous mean of sample x_t-1
													lowercase   =				scheduler.step(snake_case__			,					snake_case__			,					snake_case__			,					generator=snake_case__    ).prev_sample

													# if t > 0:
													#     noise = self.dummy_sample_deter
													#     variance = scheduler.get_variance(t) ** (0.5) * noise
													#
													# sample = pred_prev_sample + variance
													lowercase   =				pred_prev_sample

							lowercase   =				torch.sum(torch.abs(snake_case__    )    )
							lowercase   =				torch.mean(torch.abs(snake_case__    )    )

							assert abs(result_sum.item() - 202.0296    ) < 1E-2
							assert abs(result_mean.item() - 0.2631    ) < 1E-3







	def 	_a (     self    )     ->     Optional[Any]:
							'''simple docstring'''






							lowercase   =				self.scheduler_classes[0]
							lowercase   =				self.get_scheduler_config()
							lowercase   =				scheduler_class(**snake_case__    )

							lowercase   =				[100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=snake_case__    )

							lowercase   =				scheduler.timesteps

							for i, timestep in enumerate(snake_case__    ):
													if i == len(snake_case__    ) - 1:
																			lowercase   =				-1
													else:
																			lowercase   =				timesteps[i + 1]

													lowercase   =				scheduler.previous_timestep(snake_case__    )
													lowercase   =				prev_t.item()

													self.assertEqual(snake_case__			,					snake_case__    )







	def 	_a (     self    )     ->     int:
							'''simple docstring'''






							lowercase   =				self.scheduler_classes[0]
							lowercase   =				self.get_scheduler_config()
							lowercase   =				scheduler_class(**snake_case__    )

							lowercase   =				[100, 87, 50, 51, 0]

							with self.assertRaises(snake_case__			,					msg="""`custom_timesteps` must be in descending order."""    ):
													scheduler.set_timesteps(timesteps=snake_case__    )







	def 	_a (     self    )     ->     Dict:
							'''simple docstring'''






							lowercase   =				self.scheduler_classes[0]
							lowercase   =				self.get_scheduler_config()
							lowercase   =				scheduler_class(**snake_case__    )

							lowercase   =				[100, 87, 50, 1, 0]
							lowercase   =				len(snake_case__    )

							with self.assertRaises(snake_case__			,					msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""    ):
													scheduler.set_timesteps(num_inference_steps=snake_case__			,					timesteps=snake_case__    )







	def 	_a (     self    )     ->     Union[str, Any]:
							'''simple docstring'''






							lowercase   =				self.scheduler_classes[0]
							lowercase   =				self.get_scheduler_config()
							lowercase   =				scheduler_class(**snake_case__    )

							lowercase   =				[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case__			,					msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""			,					):
													scheduler.set_timesteps(timesteps=snake_case__    )



from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   42
		lowerCAmelCase_     =   42





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     int     ,  snake_case__     :     int		)      ->	Any:
				_lowerCamelCase				   =  [[] for _ in range(snake_case__		)]
				_lowerCamelCase				   =  size
		def __getitem__(       self     :     Dict     ,  snake_case__     :     int		)      ->	Iterator[Edge]:
				return iter(self._graph[vertex]		)
		@property
		def   _snake_case       (       self     :     str		)      ->	int:
				return self._size
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				if weight not in (0, 1):
						raise ValueError('Edge weight must be either 0 or 1.'		)

				if to_vertex < 0 or to_vertex >= self.size:
						raise ValueError('Vertex indexes must be in [0; size).'		)

				self._graph[from_vertex].append(Edge(snake_case__     ,  snake_case__		)		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	int | None:
				_lowerCamelCase				   =  deque([start_vertex]		)
				_lowerCamelCase				   =  [None] * self.size
				_lowerCamelCase				   =  0

				while queue:
						_lowerCamelCase				   =  queue.popleft()
						_lowerCamelCase				   =  distances[current_vertex]
						if current_distance is None:
								continue

						for edge in self[current_vertex]:
								_lowerCamelCase				   =  current_distance + edge.weight
								_lowerCamelCase				   =  distances[edge.destination_vertex]
								if (
								    isinstance(snake_case__     ,  snake_case__		)
								    and new_distance >= dest_vertex_distance
								):
										continue
								_lowerCamelCase				   =  new_distance
								if edge.weight == 0:
										queue.appendleft(edge.destination_vertex		)
								else:
										queue.append(edge.destination_vertex		)

				if distances[finish_vertex] is None:
						raise ValueError('No path from start_vertex to finish_vertex.'		)

				return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()